Pusat Pengajian Sains Komputer - Tesis
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Browsing Pusat Pengajian Sains Komputer - Tesis by Type "master thesis"
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- PublicationA Machine Learning Classification Approach To Detect Tls-Based Malware Using Entropy-Based Flow Set Features(2022-11)KinanAs internet encryption has grown to safeguard users’ privacy, malware has evolved to leverage encryption protocols such as Transport Layer Security (TLS) to conceal its hazardous connections. The difficulty and impracticality of decrypting TLS network traffic before it reaches the Intrusion Detection System (IDS) has driven numerous research studies to focus on anomaly-based malware detection without decryption employing various features and Machine Learning (ML) algorithms. Nonetheless, several of these studies used flow features with low feature importance value and poor capability to distinguish malicious flows, such as the number of packets sent and received in a flow or its duration. Furthermore, the outliers and frequency-based flow feature transformations (FTT) applied to mitigate the poor flow feature have several flaws. This thesis proposes a TLS-based malware detection (TLSMalDetect) approach based on ML classification to address flow feature utilization limitations in related work. TLSMalDetect includes periodicity-independent entropy-based flow set (EFS) features produced by an FFT technique. The efficiency of EFS features is assessed in two ways: (1) by comparing them to the relevant related work’s features of outliers and flow using four feature importance methods, and (2) by analyzing the classification performance in the scenarios with and without EFS features. This study also investigates TLSMalDetect detection performance using seven ML classification algorithms and identifies the one with the highest accuracy.
- PublicationA Visual Approach For Requirement Traceability(2022-12)Madaki, Abdulkadir AhmadRequirement traceability is a significant method of tracing and identifying the life of requirement in forward and backward directions during software development lifecycle. It is used to support impact analysis, requirement change, maintenance, verification, and validation of a software system. Visualization is one of the most suitable visual representations of requirement traceability data as it has so many aspects that can be explored. It offers detail and visible demographic symbols to show the traceability of requirements artefacts relationships. However, displaying many traceability links effectively and efficiently is a big challenge, because a software system with large numbers of artefacts and traceability links quickly gives rise to scalability and visual clutter issues. Therefore, a visual framework is designed and implemented as a tool to visualize and manage the traceability of requirements artefacts relationships. The framework follows an advance graphical user interface guide for Visual Information-Seeking which focus on overview first, zoom and filter, then details-on-demand. The tool used visualization techniques as colour-coded symbols on a node-link diagram to present data. Users can traverse the graph for an impact analysis method to understand data and make decisions during the software development life cycle. The evaluation results show a positive respond in terms of usefulness and ease of use factors. The average score mean for usefulness are 4.33 (86%), whereas the average score mean for ease of use are 4.25 (85%). This results show that the framework is useful in tracing links between requirements artefacts, easy to use as is highly effective to improve user interaction.
- PublicationAnalysis Of Feature Reduction Algorithms To Estimate Human Stress Conditions(2022-09)Arasu, Darshan BabuStress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone’s life. Thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Therefore, this study aimed to present analyse of the performance of feature classify when combining with feature selection algorithm to estimate human stress based on the facial feature of thermal imaging. Three hybrid classifiers, Support Vector Machine (SVM), Decision Tree (DT) and Logistic Regression (LR) combined with feature reduction analysis, Principal Component Analyse (PCA) and Analysis of Variance (ANOVA) was evaluated with 10-fold validation to compute classification accuracy. Four statistical features was extracted; mean, maximum, minimum and standard deviation of the gray scale value from six area regions of interest. Results showing that hybrid classifier DT-ANOVA achieves higher accuracy of 62% compared to others 90 combination classifiers. The findings demonstrated that DT-ANOVA performs well with a small dataset, while SVM and LR can improve the accuracy when fused with ANOVA for a big dataset. The findings also suggested that ANOVA can provides comparable performance as PCA.
- PublicationCompute Language Interface: A Transparent Wrapper Library For Multi Cpu-Gpu(2013-03)Ooi, Keng SiangThe Graphics Processing Unit (GPU) processing capability is getting more powerful than before. Compute intensive and data parallelism applications are proven to perform better on the GPU than on the Central Processing Unit (CPU). However, available General-Purpose Computing on Graphics Processing Unit (GPGPU) programming frameworks which are available publicly are unable to reach beyond the single computer limitation to utilize multiple CPUs and GPUs at different computers in a distributed computing system easily. This study presents the Compute Language Interface (CLI) which is a wrapper library that enables the existing OpenCL applications access to all available CPUs and GPUs in a distributed computing system through Message Passing Interface (MPI) transparently. It is designed to improve the scalability of the OpenCL applications on a distributed computing system while maintaining the same set of application programming interface (API) in the original OpenCL library. The applications can access all available CPUs and GPUs in different computers in a distributed computing system as ifall the CPUs and GPUs are in the same computer.
- PublicationCovid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)(2023-01)Ravichandran, Bhavani DeviThe spread of Covid-19 misinformation on social media had significant real-world consequences, raising fears among internet users since the pandemic has begun. Worldwide, researchers have shown an interest in developing deception classification methods to reduce the issue. This study aims to create an accurate model for the classification of Covid-19 misinformation in social media. This research has also conducted a systematic literature review to identify the most efficient method for classification with 35 papers. According to existing studies, the most efficient method for classification with the highest accuracy is the ANFIS and the DNN models. Thus, it was identified that the hybrid model of ANFIS-DNN shows the highest accuracy results. Therefore, the main goal of this study is to classify Covid-19 misinformation using an optimised hybrid model of ANFIS-DNN on social media based on the level of risk. A total of 8,000 Malaysian-based Tweets were extracted from Twitter based on topics related to Covid-19. The dataset is explored, cleaned, pre-processed, and the tweets were grouped into BoW model. Then, the proposed ANFIS-DNN is used to run the pre-processed dataset and the accuracy performance result shows 99%. Evaluation performance indexes such as confusion matrix, and accuracy are implemented in this research. The proposed model is then compared with ANFIS, DNN, Logistic Regression, SVM, Random Forest, and XGBoost. Furthermore, the accuracy is compared with other related works.
- PublicationGeographical Multicast Disruption Tolerant Networking Mechanism For Internet Of Things(2022-01)Wong Khang SiangDisruption Tolerant Networking (DTN) has been developed to overcome the intermittent connection issue between nodes in areas with poor wireless network connectivity by employing a store-carry-forward paradigm to forward messages to the destination. The existing networking protocols such as Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are not suitable since there may never be an end-toend path between the sender and the receiver. As the Internet of Things (IoT) devices proliferate, enabling DTN support in IoT environments helps bridge the communication gap between networks with intermittent connectivity such as rural areas and postdisaster scenarios. Group communication is an essential service to enable information exchange and sharing within a group and between groups in such networks. Furthermore, some applications require reliable multicast support over resource-constrained DTN networks. However, there is no well-defined standard for efficient and reliable group communication in DTN. The group communication in a post-disaster scenario that covers a large geographical area presents a more challenging environment for the disaster relief personnel to communicate and coordinate search and rescue missions. A group-based data delivery service is needed in DTN networks with multicast support for communication over multiple geographical areas. In resource-constrained IoT networks, the group-based data delivery needs to be enhanced to provide reliable multicast support for use cases, such as reliable configuration updates.
- PublicationIncorporating Informative Score For Instance Selection In Semi-supervised Sentiment Classification(2022-05)Vivian, Lee Lay ShanSentiment classification is a useful tool to classify reviews that contain a wealth of information about sentiments and attitudes towards a product or service. Existing studies are heavily relying on sentiment classification methods that require fully annotated input. However, there are limited labelled text available, making the acquirement process of the fully annotated input costly and labour intensive. In recent years, semi-supervised methods have been positively recommended as they require only partially labelled input and performed comparably to the current preferred methods. At the same time, there are some works reported the performance of semi-supervised model degraded after adding unlabelled instances into training. The contrast of the current literature shows that not all unlabelled instances are equally useful; thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model. To achieve this, informative score is proposed and incorporated into semi-supervised sentiment classification. The experiment compared the accuracy and loss of supervised method, semi-supervised method without informative score and semi-supervised method with informative score. With the help of informative score to identify informative unlabelled instances, semi-supervised models can perform better compared to semi-supervised models that do not incorporate informative score into its training. Although performance of semi-supervised models incorporated with informative score are not able to surpass the supervised models, the results are still found promising as the differences in performance are subtle and the number of labelled instances used are greatly reduced.
- PublicationOptimization Of 3d Reconstruction Surface Rendering Algorithm For Osferion Bone Void Filling(2023-09)Chin, Daniel Jie Yuan3D reconstruction visualizes the 3D models from 2D medical image slices, which is proven helpful to doctors and surgeons in diagnosing and surgical planning on OSferion bone defects, which is well known for its fast absorption rate. Among the 3D reconstruction algorithms, surface rendering algorithms are more suitable for effectively visualizing the bones’ structure and shape. However, surface rendering algorithms have two main problems, the massive number of triangular patches generated during the reconstruction process and the slow reconstruction speed, especially in reconstructing huge medical image datasets. Also, with the attempt to generate rendering-device-agnostic models by reducing the 3D models’ file size, the surface of the models is easily deformed due to the reduced number of triangular patches. Thus, the objectives are to enhance the Marching Cubes or the Marching Tetrahedra algorithm for large CT/MRI datasets so that the reconstructed 3D models are rendering-device-agnostic and optimized and to improve the quality of the 3D models after reducing the number of vertices and faces so that the surface of the 3D models can be improved. The impact of this research includes 3D models that are rendering-device-agnostic so that doctors and surgeons have access to the 3D models at anytime, anywhere. The proposed improvement method, which is Marching Cubes with 3D data smoothing and surface smoothing box kernel size of 11, mesh decimation reduction factor of 0.1, successfully increased the reconstruction accuracy by 6.26%, decreased the number of vertices and faces by 89.82%, and decreased the reconstruction and rendering time by 52.45% and 90.74% seconds respectively
- PublicationOptimizing Adaptive Neuro Fuzzy Inference System (Anfis) With Dragonfly Algorithm For Cardiovascular Disease(2023-06)Wada Mohammed JinjiriCardiovascular disease (CVD) remains a great concern in the field of healthcare. It is responsible for the highest mortality rate leading cause of death worldwide. This research utilizes an Adaptive neuro-fuzzy inference system (ANFIS) and addresses the problem of topology and parametric configurations that lead to the prediction error for CVD.
- PublicationPrivacy Preservation Model For Data Exposure In Android Smartphone Usage(2021-11)Anizah Binti Abu BakarStatistics show there are 6378 million of smartphone users. The usage of mobile applications in smartphones exposes users to privacy risks. This is due to existing works lacking a formalized mathematical model that can quantify both user and system applications risk. There is also no multifaceted data collector tool to monitor user data collection and risk posed by each application. Besides, there is no risk level benchmark that alerts users and distinguishes between acceptable and unacceptable risk levels in smartphone usage. In order to tackle the privacy risk issue, a formalized privacy model called PRiMo is proposed using tree structure and calculus knowledge to quantify the risk in each application, risk posed by each application category, and overall privacy risk faced by the smartphone user.
- PublicationReal-Time Capable Multi-Hop Media Access Control Protocol For Smart Home Environment(2017-11)M.Shukeri, NurulfaizalThe Wireless Sensor Network (WSN) is a technology that is now often highlighted and various studies have been done to apply in life such as environmental monitoring, security and military applications. These include the study of the Internet of Things (loT) and Smart Home, where it is now gaining popularity in the research environment. The combination of home appliances such as lights, gates and closed circuit, would be able to make the future home not just smart, but smarter in energy consumption and secure. Nevertheless, to apply WSN in the Smart Home Environment, WSN protocols need to support real-time characteristics. This protocol must also be capable of transmitting time-sensitive data such as audio and video at low bit rates through a multi-hop network where coverage can be expanded.
- PublicationRewind: Cbt-Based Serious Game To Improve Cognitive Emotion Regulations And Anxiety Disorders(2023-09)Heng, Yew KenReWIND is a CBT-based serious game designed specifically to improve cognitive emotion regulations and anxiety disorders of patients. The main objective of the study is to design a story-driven serious game to facilitate cognitive behavioural therapy (CBT) and measure its efficacy to complement the treatment for anxiety disorders. The foundation of the game storylines focuses on three cognitive emotion regulation strategies common in anxiety disorders: catastrophizing, rumination, and lack of refocus on planning. Each strategy consists of two scenarios remodelled from real-life incidents following Ellis’ ABCDE-model (activating event, belief, consequence, disputation, and effect). A three-phase methodology is presented in this study covering the storyline development, game development, and experimental design in detail. The mixed-design ANOVA model is used to assess the overall performance of ReWIND and test the hypotheses regarding its capability to reduce participants’ anxiety symptoms and improve cognitive emotion regulations. The outcome of this study is satisfactory as ReWIND is capable of reducing the severity level of anxiety symptoms and trait anxiety levels while increasing perceived control of anxiety better than the non-interactive task.
- PublicationSocial Sciences, Arts And Humanities Research Collaboration: The Malaysian Coverage(2014)Hussain, HusriatiAs a developing country, Malaysia emphasizes the need for university researchers to be more socially oriented and highly encourages them to seek collaboration with other organizations. Collaboration is used as the main requirement and indicator in almost Malaysian grant scheme and Malaysian research assessment, such as Higher Institutions Centre of Excellence (HICoE) Grant, Long Term Research Grant Scheme (LRGS), Malaysian Research Assessment (MyRA), National Higher Education Strategic Plan, etc. The main reasons behind this are because collaboration may contribute to creative, wealth and innovative ideas, unique experiences, and great exposures, which consequently will give a high impact on the nation's research productivity and development. In this regard, this study employed bibliometric methods of co-authorship analysis to measure the patterns of collaboration in Social Sciences, Arts and Humanities in Malaysia from 2007 to 2011. Based on the 2,280 publications, patterns of collaboration were analyzed with respect to 7 relevant areas: co-authorship patterns, national and international collaboration, country collaboration, institutional collaboration, sectoral collaboration, intra-disciplinary and interdisciplinary collaboration, and extent of collaboration. The results may help the government and universities to plan an appropriate Malaysia Research Policy and manage the R&D funding and resources effectively and efficiently. The
- PublicationText Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models(2022-08)Yong Kuan ShyangEmotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus is labour-intensive and time-consuming. Distant supervision can be used to collect large amount of training data in a short period of time using emotion word hashtags, but the collected data may contain excessive noise. In this research, we proposed a text augmentation strategy to efficiently expand the size of positive examples for six emotion categories (happiness, anger, excitement, desperation, boredom and indifference) in EmoTweet-28 by exploiting tweets collected from distant supervision (DS) that are similar to the seed examples in EmoTweet-28 (ET-seed). Similarity scoring approach was used to compute to cosine similarity scores between each DS tweet and all ET-seed tweets under the same emotion category. Seven vector representations (USE, InferSent GloVe, InferSent fastText, Word2Vec, fastText, GloVe, and Bag-of-Words) were experimented to represent the tweets in the similarity scoring approach. DS tweets with high similarity scores were selected to become the augmented instances and annotated with emotion labels. The selection of DS tweets was divided into two categories which are threshold-based selection and fixed increment selection. In addition, we also modified the proposed text augmentation strategy by altering the seed sets used for similarity scoring using clustering and misclassified strategies. All augmented sets were evaluated by training a deep neural network classifier separately to distinguish between the presence or absence of specific emotion in tweets from the test set.